Duration

Course Features

Lectures10

Quiz3

Duration
01:48:29

Skill LevelAll

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0

Language
English

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Description

Easily extract patterns and knowledge from your data using MATLAB

MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. This video will help beginners build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and you'll see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll learn about the different types of regression technique and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction to improve performance. By the end of the video, you'll have learned to put it all together via real-world use cases covering the major machine learning algorithms and will be comfortable in performing machine learning with MATLAB.

About the Author

Giuseppe Ciaburro holds a Master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a Master's degree in acoustic and noise control from Seconda Università degli Studi di Napoli. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli."

He has over 15 years' work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer courses (about 15 years), working with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching machine learning applications in acoustics and noise control.

Basic knowledge

A mathematical and statistical background will really make this video easier to follow

What will you learn

Learn the introductory concepts of machine learning

Explore the different types of regression technique such as simple and multiple linear regression, ordinary least squares estimation, correlations, and how to apply them to your data

Discover the basics of classification methods and how to implement the Naive Bayes algorithm and Decision Trees in the MATLAB environment

Perform data fitting, pattern recognition, and clustering analysis with the help of the MATLAB neural network toolbox

So far, for data organization, we have mostly used standard arrays that represent useful data structures for storing a large number of objects, but all of the same type, such as a matrix of numbers or characters. However, such arrays cannot be used if you want to memorize both numbers and strings in the same object. This is a problem that can be solved by so-called cell arrays, structure arrays, and more generally all those structures that the MATLAB programming environment provides us.

Now that you are done with the videos of section 1, let’s assess your learning. Here, are a few questions, followed by 4 options, outof which 1 is the correct option. Select the right option and validate your learning!The answers are provided in a separate sheet

Before passing our data to machine learning algorithms, we need to give a first look at what we've imported into MATLAB to see if there are any issues. To get started, it's good practice to keep your original data. To do this, every change will be performed on a copy of the dataset.

To introduce the key concepts, we will get started with a simple linear regression example. We just use a spreadsheet that contains the number of vehicles registered in Italy and the population of the different regions.

Calculate the number of vehicles checked for each state from the state's population

Calculate the difference between the observed values and the fitted values

Calculate the determination coefficient to compare the performance of the two models

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